The Why, What and How of Artificial General Intelligence Chip Development
This paper addresses the foundational challenge of developing hardware for Artificial General Intelligence, which is crucial for the future of AI systems.
This paper explores the transition from application-specific AI chips to Artificial General Intelligence (AGI) chips, driven by the need for human-like generalization, performance, robustness, and scalability. It provides an overview of emerging AI chip technologies, classifies edge AI implementations, and outlines a funnel design flow for AGI chip development.
The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilities. Such a requirement to transit from application-specific to general intelligence AI chip must consider several factors. This paper provides an overview of this cross-disciplinary field of study, elaborating on the generalisation of intelligence as understood in building artificial general intelligence (AGI) systems. This work presents a listing of emerging AI chip technologies, classification of edge AI implementations, and the funnel design flow for AGI chip development. Finally, the design consideration required for building an AGI chip is listed along with the methods for testing and validating it.